Accurate design of high-affinity protein binding macrocycles using deep learning.

The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest.

Dynamic dysregulation of retrotransposons in neurodegenerative diseases at the single-cell level.

Retrotransposable elements (RTEs) are common mobile genetic elements comprising ∼42% of the human genome. RTEs play critical roles in gene regulation and function, but how they are specifically involved in complex diseases is largely unknown. Here, we investigate the cellular heterogeneity of RTEs using 12 single-cell transcriptome profiles covering three neurodegenerative diseases, Alzheimer's disease (AD), Parkinson's disease, and multiple sclerosis.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease.

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate -regulatory elements (cCREs) across brain regions.

Single-nucleus multiomics reveals the disrupted regulatory programs in three brain regions of sporadic early-onset Alzheimer's disease.

Sporadic early-onset Alzheimer's disease (sEOAD) represents a significant but less-studied subtype of Alzheimer's disease (AD). Here, we generated a single-nucleus multiome atlas derived from the postmortem prefrontal cortex, entorhinal cortex, and hippocampus of nine individuals with or without sEOAD. Comprehensive analyses were conducted to delineate cell type-specific transcriptomic changes and linked candidate regulatory elements (cCREs) across brain regions.

Investigating gene functions and single-cell expression profiles of de novo variants in orofacial clefts.

Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs.

Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach.

Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

Background: The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective: To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association.

Background: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

Methods: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples.

Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: An integrative study of single-nucleus transcriptomes and genetic association.

Background: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

Methods: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach.

Background: The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points.

Methods: We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.

Background: Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease.

Efficient degradation of 2,4-dichlorophenol in water by sequential electrocatalytic reduction and oxidation with a Pd-MWCNTs/Ni-foam electrode.

Our previous study indicated excellent dechlorination efficiency and phenol conversion rate in the electrocatalytic reduction of 2,4-dichlorophenol (2,4-DCP) with a Pd-MWCNTs/Ni-foam electrode; it is deserved to investigate whether this electrode can efficiently degrade phenol in electro-Fenton oxidation (EFO) process and realize the effective mineralization of 2,4-DCP in aqueous solution. In this work, the sequential electrocatalytic reduction and oxidation of 2,4-DCP were studied after examining phenol degradation in the EFO process. The results showed that the removal efficiency of 0.

Interactions and quantification of multiple influencing factors on cadmium accumulation in soil-rice systems at a large region.

The accumulation of Cd in soil-rice systems at a large region is often extremely complicated due to environmental heterogeneity and the interactions of multiple influencing factors. However, the interactive effects and quantification of the contributions of influencing factors on Cd accumulation in large regions remain unclear. In this study, conditional inference trees and random forest analysis were used to identify the interactions of various factors (soil properties, topography and demographic-economic), and quantify their contributions to Cd accumulation in soil-rice systems of Sichuan-Chongqing region, China.

Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.

The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications.

A Visible-Light-Promoted C-H Arylation and Heteroarylation of Uracil Derivatives with Diazoniums in Aqueous Conditions.

The photoredox synthesis of C-5 (hetero)arylated uracil and uridine substrates with the corresponding diazonium salts is described. The coupling proceeds efficiently without protection of the hydroxyls at the ribose or pre-functionalization of the C5 position at the nucleobase. No transition-metal catalyst is used in this transformation, thereby avoiding metal contamination in the final products.

Identifying candidate genes and drug targets for Alzheimer's disease by an integrative network approach using genetic and brain region-specific proteomic data.

Genome-wide association studies (GWAS) have identified more than 75 genetic variants associated with Alzheimer's disease (ad). However, how these variants function and impact protein expression in brain regions remain elusive. Large-scale proteomic datasets of ad postmortem brain tissues have become available recently.

WebCSEA: web-based cell-type-specific enrichment analysis of genes.

Human complex traits and common diseases show tissue- and cell-type- specificity. Recently, single-cell RNA sequencing (scRNA-seq) technology has successfully depicted cellular heterogeneity in human tissue, providing an unprecedented opportunity to understand the context-specific expression of complex trait-associated genes in human tissue-cell types (TCs). Here, we present the first web-based application to quickly assess the cell-type-specificity of genes, named Web-based Cell-type Specific Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.

Prioritization of risk genes in multiple sclerosis by a refined Bayesian framework followed by tissue-specificity and cell type feature assessment.

Background: Multiple sclerosis (MS) is a debilitating immune-mediated disease of the central nervous system that affects over 2 million people worldwide, resulting in a heavy burden to families and entire communities. Understanding the genetic basis underlying MS could help decipher the pathogenesis and shed light on MS treatment. We refined a recently developed Bayesian framework, Integrative Risk Gene Selector (iRIGS), to prioritize risk genes associated with MS by integrating the summary statistics from the largest GWAS to date (n = 115,803), various genomic features, and gene-gene closeness.

[Cd Pollution and Safe Planting Zoning in Paddy Soils: A Case Study in a District of Chongqing].

Rice has a strong ability to accumulate Cd in soil, and it is of great significance to study Cd pollution and safe planting zoning in paddy soils. In this work, 300 sets of paddy soil-rice samples were simultaneously collected in 22 towns in a District of Chongqing, and soil pH, soil total and available Cd contents, and brown rice Cd contents were determined. Soil Cd pollution was assessed using the geoaccumulation index, bioconcentration factor, and the single-factor pollution index.